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Spectral variance for a GALFORM + GRASIL catalogue =>ANN: Mathematical algorithms for data analysis, introduced to replicate the brain behavior - learn from examples SEDs : complex, non-linear, high dimensionality and large variance functions of some galaxy properties ANN is a black box that is trained to predict the SED from controlling parameters using a suitable precomputed training set (many couples input-output) Improving the computing time: Modelling SEDs with Artificial Neural Neworks

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Input layer: parameters determining the SED Output layer: SED, one unit for each L( ) Hidden layers: black box! w jk n j =  w jk i k o j =f(n j ) Propagation rule: the output from each unit is weighted and summed to form the input for the upper layer units: n j =  w jk i k The new output is o j =f(n j ), f=non linear function Learning: the ANN is trained with a given target- weights are adjusted to best approximate a given set of inputs/outputs

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Summary Multi-wavelength modelling as a tool to quantitatively deconvolve/ interpret observations – make predictions/ constrain galaxy formation models Different treatments predict different SEDs for the same SFR(t)-> necessity of a reliable computation of the SED for proper interpretations of observations and predictions of galaxy formation models The treatment of dust reprocessing of UV/optical in the IR requires a proper computation – time cosuming for some applications For large cosmological applications: promising solution with ANN